1. Field of the Invention
The present invention relates to a vector discrimination apparatus,for performing discrimination such as class classification and recognition of vectors consisting of a single or plurality of vector components corresponding to features of predetermined information such as an image (e.g., a character or any other figure), various pieces of voice information, and technical information to be retrieved, and for performing discrimination such as class classification and recognition of the image, voice, and technical information to be retrieved.
2. Description of the Prior Art
A conventional image recognition apparatus for recognizing an image such as a character or any other figure is designed to perform digital processing utilizing primarily electronic techniques. Best matching between data corresponding to the feature of the image to be recognized and corresponding pieces of reference data stored in a data base is obtained by correlation calculations.
However, in case character recognition in which an image to be recognized is, for example, a character image, even if printed characters having a predetermined form are recognized, Japanese hiragana characters are not suitably recognized automatically by an image recognition apparatus since they include many curved portions. On the other hand, Chinese characters have a large number of strokes and are complicated. In addition, the kinds of characters are several thousands, and the printed characters include many styles such as a Gothic type and a Ming type. Handwritten characters have various styles depending on writers. In any case, the operation of the image recognition apparatus is very complicated. It is very difficult to improve recognition precision while the operation speed is kept at a high speed.
A large number of existing characters include similar characters and characters whose parts are very similar to each other or the same. It is thus possible to classify the characters into a plurality of classes.
In the image recognition apparatus, features of an image to be recognized are extracted by projection or the like, and the extracted projection features are compared with those of a reference pattern for class classification, thereby classifying the characters into classes. The class-classified images are then correlated with a large number of images belonging to an identical class according to correlation calculations. The image can be specified, i.e., recognized. In the above correlation calculation process, the projection features of an image to be recognized are compared with those of a large number of image recognition reference patterns (standard patterns). The large number of standard patterns are ordered according to the degree of similarity to the image to be recognized. The image of interest is then specified by this ordering.
A conventional image recognition apparatus designed to perform digital processing utilizing primarily electronic techniques will be described in more detail below. An image pattern subjected to image recognition and written on an original by printing or the like is focused by an optical lens on a light-receiving surface of an image sensor comprising a CCD or a MOS sensor. A multi-value digital signal as image information is output from the image sensor and is binarized by a proper threshold value (if there are a plurality of threshold values, multi-value conversion different from that described above is performed). The binarized signal is stored in a memory. The binarized image information is subjected to preprocessing for shaping the image, as needed. The preprocessed image information is stored in the above memory or another memory. Preprocessing includes noise reduction processing and normalization processing for positions, sizes, inclinations, and widths.
A projection feature required for discriminating an image is extracted by a projection-processing section from the image information stored in the memory.
In order to project an image on a given axis (e.g., the X-axis), the memory which stores the image information is scanned in a direction (e.g., the Y-axis) having a predetermined relationship with the given axis, and the image information is read out in time series or simultaneous time series. The readout image information is transferred to the projection-processing section. Pieces of the transferred image information are sequentially accumulated. Accumulated values sequentially obtained by such accumulations are stored at predetermined positions corresponding to the given axis in the memory or in another memory. A curve of an intensity distribution obtained by extracting projection features on the given axis is calculated on the basis of the stored accumulated values.
The projection features of the image are normally extracted on a plurality of given axes, and thus a plurality of intensity distribution curves can be obtained for identical image information. The plurality of projection features of the image which are represented by these intensity distribution curves are compared with projection features of a prestored standard pattern, thereby classifying the image into classes or recognizing the image.
In order to digitally process the projection features described above, the accumulated values as input data are regarded as one vector component. One or a plurality of intensity distributions is dealt as one vector having a large number of vector components. Therefore, if the accumulated values for the intensity distribution are stored at addresses 1 to n, this intensity distribution constitutes an n-dimensional vector.
In the image recognition apparatus described above, in order to increase an image recognition rate, projection processing must be performed for the same image information on a large number of axes to extract various types of projection features. Therefore, the image to be recognized is dealt as a set of a large number of multi-dimensional vectors.
The reference pattern for class classification and image recognition is transformed into vector in the same manner as described above. Vector calculators practically used in a parallel pipeline type computer calculate correlations for each vector component between a large number of reference pattern vectors and the vector corresponding to the features of the image to be recognized.
In the correlation calculations, for example, a distance between two vectors, a correlation coefficient, or the degree of similarity is calculated as a factor representing the degree of correlation. In order to determine the degree of correlation, an optimal correlation must be found in consideration of variations in features such as projection features or the like. In practice, correlation calculations between a large number of vectors are repeated according to time-serial digital processing.
In the conventional image recognition apparatus described above, a large number of reference patterns for class classification or image recognition must be previously transformed into vectors. In addition, the correlation calculations between a large number of reference pattern vectors and the vector corresponding to the features of the image to be recognized must be performed by the above vector calculators according to time-serial digital processing for each vector component.
A long processing time is required for the vector transform and correlation calculations. The vector calculator must incorporate a exclusive processor using a exclusive LSI, and the system configuration is complicated, thus increasing cost.